CIESC Journal ›› 2024, Vol. 75 ›› Issue (9): 3242-3254.DOI: 10.11949/0438-1157.20240324
• Process system engineering • Previous Articles Next Articles
Xin GUO1,2,3,4(), Wenjing LI2,3,4, Junfei QIAO2,3,4
Received:
2024-03-20
Revised:
2024-05-21
Online:
2024-10-10
Published:
2024-09-25
Contact:
Xin GUO
郭鑫1,2,3,4(), 李文静2,3,4, 乔俊飞2,3,4
通讯作者:
郭鑫
作者简介:
郭鑫(1990—),男,博士,讲师,guo_xin@haut.edu.cn
基金资助:
CLC Number:
Xin GUO, Wenjing LI, Junfei QIAO. Prediction of effluent parameters in wastewater treatment process using self-organizing modular neural network[J]. CIESC Journal, 2024, 75(9): 3242-3254.
郭鑫, 李文静, 乔俊飞. 基于自组织模块化神经网络的污水处理过程出水参数预测[J]. 化工学报, 2024, 75(9): 3242-3254.
子网络模型 | 预测RMSE×103 | ||||
---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | |
EMD-SMNN | 2.3 | 2.2 | 0.7 | 0.6 | 0.4 |
EMD-MNN | 3.0 | 5.7 | 0.6 | 0.6 | 0.5 |
Table 1 Prediction RMSE of sub-networks
子网络模型 | 预测RMSE×103 | ||||
---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | |
EMD-SMNN | 2.3 | 2.2 | 0.7 | 0.6 | 0.4 |
EMD-MNN | 3.0 | 5.7 | 0.6 | 0.6 | 0.5 |
网络类型 | 模块数 | 隐含层节点数 | RMSE | NMSE |
---|---|---|---|---|
EMD-SMNN | 5 | 12 | 4.0×10-3 | 3.1×10-4 |
EMD-MNN | 5 | 15 | 4.5×10-3 | 3.3×10-4 |
SWEMD-MNN[ | 5 | 15 | 7.0×10-3 | 9.8×10-4 |
OAMNN[ | 5 | 25 | 8.6×10-3 | — |
CMNN[ | 4 | 12 | 1.9×10-2 | 5.5×10-3 |
OSAMNN[ | 7 | 35 | 3.1×10-2 | — |
FNN | 1 | 15 | 4.1×10-2 | 6.1×10-3 |
CCRNN[ | 1 | 11 | 9.3×10-3 | 6.3×10-4 |
CICC[ | 1 | 9 | 8.5×10-3 | — |
CCPSO[ | 1 | — | 8.2×10-3 | — |
Table 2 Prediction results of different models for Mackey-Glass time series
网络类型 | 模块数 | 隐含层节点数 | RMSE | NMSE |
---|---|---|---|---|
EMD-SMNN | 5 | 12 | 4.0×10-3 | 3.1×10-4 |
EMD-MNN | 5 | 15 | 4.5×10-3 | 3.3×10-4 |
SWEMD-MNN[ | 5 | 15 | 7.0×10-3 | 9.8×10-4 |
OAMNN[ | 5 | 25 | 8.6×10-3 | — |
CMNN[ | 4 | 12 | 1.9×10-2 | 5.5×10-3 |
OSAMNN[ | 7 | 35 | 3.1×10-2 | — |
FNN | 1 | 15 | 4.1×10-2 | 6.1×10-3 |
CCRNN[ | 1 | 11 | 9.3×10-3 | 6.3×10-4 |
CICC[ | 1 | 9 | 8.5×10-3 | — |
CCPSO[ | 1 | — | 8.2×10-3 | — |
子网络模型 | 预测RMSE×102 | ||||
---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | |
EMD-SMNN | 0.8 | 0.5 | 0.6 | 0.2 | 0.1 |
EMD-MNN | 1.3 | 0.6 | 0.6 | 0.7 | 0.1 |
Table 3 Prediction RMSE of sub-networks
子网络模型 | 预测RMSE×102 | ||||
---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | |
EMD-SMNN | 0.8 | 0.5 | 0.6 | 0.2 | 0.1 |
EMD-MNN | 1.3 | 0.6 | 0.6 | 0.7 | 0.1 |
网络类型 | 模块数 | 隐含层节点数 | RMSE | NMSE |
---|---|---|---|---|
EMD-SMNN | 5 | 16 | 0.8×10-3 | 1.1×10-4 |
EMD-MNN | 5 | 15 | 1.1×10-2 | 1.3×10-4 |
CMNN[ | 4 | 12 | 2.1×10-2 | 2.0×10-3 |
OAMNN[ | 4 | 20 | 1.3×10-2 | — |
FNN | 1 | 15 | 7.3×10-2 | 2.2×10-2 |
CCRNN[ | 1 | 13 | 1.9×10-2 | 8.2×10-3 |
CICC[ | 1 | 9 | 2.4×10-2 | — |
MLP-BLM[ | 1 | 37 | — | 9.6×10-4 |
Table 4 Pediction results of different models for Lorenz time series
网络类型 | 模块数 | 隐含层节点数 | RMSE | NMSE |
---|---|---|---|---|
EMD-SMNN | 5 | 16 | 0.8×10-3 | 1.1×10-4 |
EMD-MNN | 5 | 15 | 1.1×10-2 | 1.3×10-4 |
CMNN[ | 4 | 12 | 2.1×10-2 | 2.0×10-3 |
OAMNN[ | 4 | 20 | 1.3×10-2 | — |
FNN | 1 | 15 | 7.3×10-2 | 2.2×10-2 |
CCRNN[ | 1 | 13 | 1.9×10-2 | 8.2×10-3 |
CICC[ | 1 | 9 | 2.4×10-2 | — |
MLP-BLM[ | 1 | 37 | — | 9.6×10-4 |
子网络模型 | 预测RMSE×102 | ||||||
---|---|---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | M6 | M7 | |
EMD-SMNN | 1.8 | 0.9 | 0.4 | 0.2 | 0.2 | 0.3 | 0.3 |
EMD-MNN | 2.3 | 1.0 | 0.9 | 0.3 | 0.2 | 0.8 | 0.4 |
Table 5 Prediction RMSE of sub-networks
子网络模型 | 预测RMSE×102 | ||||||
---|---|---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | M6 | M7 | |
EMD-SMNN | 1.8 | 0.9 | 0.4 | 0.2 | 0.2 | 0.3 | 0.3 |
EMD-MNN | 2.3 | 1.0 | 0.9 | 0.3 | 0.2 | 0.8 | 0.4 |
网络类型 | 模块数 | 隐含层节点数 | RMSE | NMSE |
---|---|---|---|---|
EMD-SMNN | 8 | 24 | 1.0×10-1 | 1.0×10-2 |
EMD-MNN | 8 | 24 | 1.5×10-1 | 1.6×10-2 |
SWEMD-MNN[ | 8 | 24 | 1.4×10-1 | 2.4×10-2 |
OAMNN[ | 5 | 25 | 2.1×10-1 | — |
CMNN[ | 8 | 40 | 2.5×10-1 | 7.4×10-2 |
OSAMNN[ | 7 | 35 | 2.6×10-1 | — |
FNN | 1 | 20 | 7.4×10-1 | 8.4×10-2 |
Table 6 Prediction results of different models for effluent NH4-N
网络类型 | 模块数 | 隐含层节点数 | RMSE | NMSE |
---|---|---|---|---|
EMD-SMNN | 8 | 24 | 1.0×10-1 | 1.0×10-2 |
EMD-MNN | 8 | 24 | 1.5×10-1 | 1.6×10-2 |
SWEMD-MNN[ | 8 | 24 | 1.4×10-1 | 2.4×10-2 |
OAMNN[ | 5 | 25 | 2.1×10-1 | — |
CMNN[ | 8 | 40 | 2.5×10-1 | 7.4×10-2 |
OSAMNN[ | 7 | 35 | 2.6×10-1 | — |
FNN | 1 | 20 | 7.4×10-1 | 8.4×10-2 |
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